axiom-mcp 0.1.0

Model Context Protocol (MCP) implementation for connecting AI systems with external data sources

Axiom MCP

Model Context Protocol (MCP) implementation for connecting AI systems with external data sources.

Installation

Using uv (recommended):

uv pip install axiom-mcp

Using pip:

pip install axiom-mcp

Development Setup

1. Clone the repository:

   git clone https://github.com/yourusername/axiom-mcp.git
   cd axiom-mcp
   

2. Install uv if you haven't already:

   curl -LsSf https://astral.sh/uv/install.sh | sh
   

3. Create and activate a virtual environment with uv:

   uv venv
   source .venv/bin/activate  # On Unix/Linux
   # or
   .venv\Scripts\activate  # On Windows
   

4. Install development dependencies:

   uv pip install -e ".[dev]"
   

5. Install pre-commit hooks:

   pre-commit install
   

Pre-commit Hooks

This project uses pre-commit hooks to ensure code quality. The following checks are run automatically before each commit:

• Code formatting with black
• Import sorting with isort
• Type checking with mypy
• Linting with ruff
• Basic syntax checks
• Check for large files
• Check for merge conflicts

To manually run all pre-commit hooks:

uv run pre-commit run --all-files

Running Tests

uv run python -m pytest

For tests with coverage report:

uv run python -m pytest --cov=axiom_mcp tests/

Code Quality

This project enforces high code quality standards using:

• black for consistent code formatting
• isort for import sorting
• mypy for static type checking
• ruff for fast Python linting
• pytest for testing with coverage reporting

To run all quality checks:

black .
isort .
mypy .
ruff .
pytest

Using uv for Fast Dependencies Management

uv provides faster package installation and dependency resolution. Some useful commands:

# Update dependencies
uv pip compile pyproject.toml -o requirements.txt

# Sync your environment with requirements
uv pip sync requirements.txt

# Add a new dependency
uv pip install package-name

Contributing

1. Fork the repository
2. Create a new branch for your feature
3. Make your changes
4. Run all quality checks:

   pre-commit run --all-files
   pytest
   

5. Submit a pull request

License

MIT License